% 定义障碍物参数
obstacles = [
    struct('type', 'building', 'position', [100, 100, 50], 'radius', 50),
    struct('type', 'airflow', 'position', [200, 200, 100], 'radius', 50),
    struct('type', 'gravity', 'position', [300, 300, 150], 'radius', 50)
];

% 定义目标参数
targets = [
    struct('position', [400, 400, 100], 'velocity', [10, 10, 10]),
    struct('position', [500, 500, 100], 'velocity', [-10, -10, -10])
];


% 定义障碍物和环境参数
obstacles = load('obstacles.mat');
targets = load('targets.mat');

% 初始化飞行器状态
position = [0, 0, 0];  % 位置 [x, y, z]
velocity = [0, 0, 0];  % 速度 [vx, vy, vz]

% 仿真时间步长
dt = 0.1;

% 仿真时间
t = 0;

% 仿真数据存储
positions = [];
velocities = [];

% 仿真循环
while t < 100
    % 获取障碍物和目标的感知数据
    [obstacle_detections, target_detections] = get_perceptions(position, obstacles, targets);
    
    % 基于感知数据做出决策
    [new_velocity, new_direction] = make_decision(obstacle_detections, target_detections);
    
    % 更新飞行器状态
    position = position + velocity * dt;
    velocity = new_velocity;
    
    % 存储数据
    positions = [positions; position];
    velocities = [velocities; velocity];
    
    % 更新时间
    t = t + dt;
end

% 绘制数据可视化

% 3D 绘图
figure;
hold on;
grid on;
xlabel('X');
ylabel('Y');
zlabel('Z');
title('飞行器仿真');

% 绘制障碍物
for i = 1:length(obstacles)
    obstacle = obstacles(i);
    switch obstacle.type
        case 'building'
            color = 'r';
        case 'airflow'
            color = 'g';
        case 'gravity'
            color = 'b';
    end
    sphere(obstacle.position(1), obstacle.position(2), obstacle.position(3), obstacle.radius, 20);
end

% 绘制目标
for i = 1:length(targets)
    target = targets(i);
    plot3(target.position(1), target.position(2), target.position(3), 'bo');
end

% 绘制飞行器轨迹
plot3(positions(:, 1), positions(:, 2), positions(:, 3), 'k-');

% 绘制飞行器速度向量
quiver3(positions(:, 1), positions(:, 2), positions(:, 3), velocities(:, 1), velocities(:, 2), velocities(:, 3), 'm');

hold off;

% 2D 位置和速度图
figure;

% 位置图
subplot(2, 1, 1);
plot(positions(:, 1), positions(:, 2), 'r-');
xlabel('时间');
ylabel('X');
title('位置');

subplot(2, 1, 2);
plot(positions(:, 1), positions(:, 3), 'b-');
xlabel('时间');
ylabel('Z');

% 速度图
subplot(2, 1, 3);
plot(velocities(:, 1), velocities(:, 2), 'r-');
xlabel('时间');
ylabel('X');
title('速度');

subplot(2, 1, 4);
plot(velocities(:, 1), velocities(:, 3), 'b-');
xlabel('时间');
ylabel('Z');

function [obstacle_detections, target_detections] = get_perceptions(position, obstacles, targets)
    % 获取障碍物检测结果
    obstacle_detections = [];
    for i = 1:length(obstacles)
        obstacle = obstacles(i);
        distance = norm(position - obstacle.position);
        if distance < obstacle.radius
            obstacle_detections = [obstacle_detections; obstacle];
        end
    end

    % 获取目标检测结果
    target_detections = [];
    for i = 1:length(targets)
        target = targets(i);
        distance = norm(position - target.position);
        if distance < 100
            target_detections = [target_detections; target];
        end
    end
end

function [new_velocity, new_direction] = make_decision(obstacle_detections, target_detections)
    % 基于感知数据做出决策
    % ...

    % 更新速度和方向
    new_velocity = [0, 0, 0];
    new_direction = [0, 0, 0];
end

function sphere(x, y, z, radius, segments)
    [X, Y, Z] = sphere_points(x, y, z, radius, segments);
    surf(X, Y, Z, 'FaceColor', 'blue', 'EdgeColor', 'none');
end

function [X, Y, Z] = sphere_points(x, y, z, radius, segments)
    % 生成球体表面的点
    u = linspace(0, 2 * pi, segments);
    v = linspace(0, pi, segments);
    
    X = x + radius * cos(u) .* sin(v);
    Y = y + radius * sin(u) .* sin(v);
    Z = z + radius * cos(v);
end
